CONICET.
Department of Psychology, University of Haifa.
J Consult Clin Psychol. 2019 Jul;87(7):617-628. doi: 10.1037/ccp0000417.
The aim of this study was to identify differential baseline profiles of interpersonal problems in patients with emotional disorders and investigate their ability to predict the extent to which alliance is important for early treatment outcome in therapy.
Ninety-six patients diagnosed with emotional disorders were admitted to psychotherapy at an independent practice center. After the first session, participants completed the Inventory of Interpersonal Problems and, after each of the first four sessions, the Alliance Negotiation Scale and the Outcome Questionnaire. We characterized the interpersonal problems of the sample using the circular statistics and the structural summary methods. Based on evidence of heterogeneity between patients, we conducted cluster analysis to identify differential profiles of interpersonal problems. We tested whether the identified profiles can predict the strength of the association between alliance negotiation and early treatment outcome using hierarchical linear models.
A two-cluster solution showed the best fit for the data. One cluster was characterized by Cold interpersonal problems (too hostile) and the other by Overly Nurturant interpersonal problems (too dependent). The identified profiles were significant predictors of the early alliance negotiation-outcome association. Overly Nurturant patients showed greater early improvements in outcome in the face of a stronger alliance negotiation.
Results support the importance of personalized approaches using patients' interpersonal profiles to determine the importance of alliance negotiation for early treatment outcome. Findings should be replicated in randomized controlled trials using strategies to manipulate alliance negotiation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
本研究旨在识别情感障碍患者人际问题的基线差异特征,并探讨其预测联盟对治疗早期疗效的重要性的能力。
96 名被诊断为情感障碍的患者在一家独立的实践中心接受心理治疗。在第一次治疗后,参与者完成了人际关系问题清单,并在第一次治疗的前四次治疗后,完成了联盟协商量表和治疗结果问卷。我们使用循环统计学和结构总结方法对样本的人际问题进行了特征描述。基于患者之间存在异质性的证据,我们进行了聚类分析,以识别人际问题的差异特征。我们使用分层线性模型,测试了所识别的特征是否可以预测联盟协商与早期治疗结果之间的关联强度。
双聚类解决方案最适合数据。一个聚类的特点是 Cold 人际问题(过于敌对),另一个聚类的特点是 Overly Nurturant 人际问题(过于依赖)。所识别的特征是早期联盟协商-结果关联的显著预测因子。面对更强的联盟协商,过度养育型患者在早期的治疗结果上表现出更大的改善。
结果支持使用患者的人际特征来确定联盟协商对早期治疗结果的重要性的个性化方法的重要性。应在随机对照试验中使用操纵联盟协商的策略对该发现进行复制。(美国心理协会,2019 年)